Szanowni Państwo,

Z przyjemnością informuję, że otworzyliśmy nabór do Programu BioLAB 2023-24

Main supervisor: Kristoffer Sahlin (ksahlin@math.su.se) Overview We see a rapid increase in generated DNA and RNA sequencing data with recent sequencing technology developments. As DNA and RNA datasets and databases grow, computational algorithms to index and search the datasets need to improve not o become a bottleneck. Recently, several novel methods for sequence similarity searches in such datasets have been proposed. One such method is strobemers [1], which can be used for general-purpose text similarity matching. Strobemers was published at a proof-of-concept level [1] and has shown to be a very efficient method to use in foundational algorithms in bioinformatics such as read alignment [2]. Strobemers is a seeding technique conceptually different from previous approaches and has opened up several new directions to study this class of indexing methods further. At a high level, a strobemer is a gapped seed formed from n linked substrings of length k (k-mers) that occurs close by in the text (within a distance [w_min, w_max]). Strobemers are specified by four parameters (n, k, w_min, w_max), together with a hash function that determines which k-mers to link. 1. Sahlin, Effective sequence similarity detection with strobemers, Genome Res. November 2021 31:2080-2094; doi:10.1101/gr.275648.121. 2. Sahlin, Flexible seed size enables ultra-fast and accurate read alignment 2022, bioRxiv; doi:https://doi.org/10.1101/2021.06.18.449070.

Project 1: Exploring representations and parametrizations of strobemers for sequence similarity searches In this project, we aim to study further the properties of strobemers, such as (i) optimal parameters for various sequence diversity levels (ii) which hash methods are best for efficient pseudo-random generation of strobes (iii) alternate strobemer representations (as fuzzy seed or linked k-mers) and which is best for various applications (iv) exploration of alternative constructions.

Project 2: Algorithms for efficient biological sequence similarity searches This project aims to utilize and adapt strobemers for sequence similarity search in various bioinformatics algorithms. There are several avenues to explore, such as long-read mapping, genome assembly, and sequence clustering. Compared to project 1, this project is more focused on end-to-end algorithm development and will use several additional computational techniques.

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Main supervisor: Marc Hellmuth (marc.hellmuth@math.su.se) Co-supervisor: Lars Arvestad (arve@math.su.se) Project: Computational and mathematical studies of ancient DNA In this PhD project, you will develop mathematical and computational tools for the study of ancient DNA. The project is conducted in collaboration with Anders Götherström (Dept of Archaeology) and Love Dalén (Dept of Zoology and the Swedish Museum of Natural History), who founded Center for Palaeogenetics at Stockholm University. Computation and data availability has changed biology and medicin, and enabled entirely new scientific questions to be asked. In palaeogenomics, the quality of DNA is fairly low which causes extra challenges for analysis, because the standard tools are not constructed for low-quality data and the specific data characteristics of ancient DNA. We are looking for candidates with a strong background in Bioinformatics, Mathematics, Statistics, and Computer Science. The work will include programming, practical data analysis, as well as theoretical analysis.

Rozpoczął się nabór wniosków do Programu BioLAB 2022-23, Program BioLAB to roczny staż dla studentów nauk biologicznych (biologia, biochemia, biotechnologia, chemia, fizyka, farmacja, itp.) w czterech amerykańskich instytucjach naukowych:

✔️ University of Chicago
✔️ University of Virginia
✔️ University of Texas Southwestern Medical Center
✔️ Oklahoma Medical Research Foundation

Czytaj więcej: Roczny, płatny staż badawczy dla studentów i studentek nauk biologicznych, medycznych i pokrewnych